As we’ve covered before on the blog, Mobile Marketing is widely used now by many different actors, which is great to see, but it is still difficult to send personalized interactions and specifically tailored offers to the end-users. Consequently, threshold limits of campaign responses/acceptance rates tend to be relatively low.

To overcome this threshold and boost campaign success rates, it is crucial to get to know our customers and targets more deeply and take careful note of their purchasing habits, their wider interests and preferences. From here we can then put forward offers that are highly relevant to them. This is why using machine learning is a key tool and will help marketers improve their #DeepLearning by turning raw data into valuable insights.

In other, simpler words, Machine Learning is about building/recognizing patterns on historical data to deliver predictions.

In Mobile Marketing, Machine Learning algorithms can learn some properties of previous mobile marketing campaign results, and then apply these to predict the #NextBestOffer for the end users.

Broadly speaking, the key objective any marketers would like to reach with their Mobile Marketing Campaigns is an improvement in the acceptance rate of their campaign by predicting the rating a user would give to an offer. Of course, this isn’t easy.

So, how can they reach this objective? The answer, as you may have guessed, is using a Machine Learning technique called a Recommendation System. This is an information filtering system that builds predictions based on historical data of previous campaigns. The system works through three different techniques.

Recommendation System techniques

Collaborative filtering: Automatic predictions about the interests of a user by collecting preferences from many users (collaborating). Underlying assumption is that if a person A accepts the same offer as a person B, A is more likely to behave like B for another offer than that of a randomly chosen person.

How can the processes be improved?

Beyond campaign results, increasing sources of relevant data can boost model accuracy considerably. In short, the higher quality you put in (the greater number of relevant data sources you input), the higher your acceptance rate is. So, in the future, we can imagine adding other users’ insights and sources in our data lake to improve the efficiency of our predictive marketing tools, such as data coming from operators.

Related posts:

Breach notifications are already rolling into the Australian government in wake of new data protection regulations. On February 22, 2018, the Privacy Amendment (Notifiable Data Breaches) Act of 2017 took…